{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Open the info file\n",
    "def load(filename):\n",
    "    with open(filename, \"rb\") as f:\n",
    "        while True:\n",
    "            try:\n",
    "                yield pickle.load(f)\n",
    "            except EOFError:\n",
    "                break\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_epochs_per_k(output_file):\n",
    "    items = load(output_file)\n",
    "    epochs_per_k = {}\n",
    "    max_k=100\n",
    "\n",
    "    for k in range(1,max_k):\n",
    "        #print k\n",
    "        _epochs = []\n",
    "        items = load(output_file)\n",
    "        for item in items:\n",
    "            #print item\n",
    "            markovity = item['markovity']\n",
    "            #print k,markovity\n",
    "            if markovity==k:\n",
    "                #print k\n",
    "                _epochs.append(item['epoch_stopped'])\n",
    "\n",
    "        if len(_epochs):\n",
    "            _array = np.asarray(_epochs)\n",
    "            #print _array\n",
    "            epochs_per_k[k] = np.mean(_array)\n",
    "    return epochs_per_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFkCAYAAAB1rtL+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD39WaHPXXoohAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d9a9c51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "#print epochs_per_k\n",
    "output_file = \"output_0entropy_3.txt\"\n",
    "epochs_per_k = get_epochs_per_k(output_file)\n",
    "epoch_stopped = sorted(epochs_per_k.items())\n",
    "x1, y1 = zip(*epoch_stopped)\n",
    "plt.plot(x1, y1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment with 0 Entropy sources\n",
    "\n",
    "Sources such as:\n",
    "$$ X_n = X_{n-1} + X_{n-k}$$\n",
    "The X-axis is the $k$ markovity, and Y-axis the number of epochs for convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFkCAYAAAB1rtL+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d9a694550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "#print epochs_per_k\n",
    "output_file = \"output_0entropy_4.txt\"\n",
    "epochs_per_k = get_epochs_per_k(output_file)\n",
    "epoch_stopped = sorted(epochs_per_k.items())\n",
    "x, y = zip(*epoch_stopped)\n",
    "plt.plot(x, y,'r')\n",
    "plt.plot(x1,y1,'b')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python_with_tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
